Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ilexlyu/ad-proposal-skills --skill ad-delivery-reviewgit clone --depth 1 https://github.com/ilexlyu/ad-proposal-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ilexlyu/ad-proposal-skills/ad-delivery-review)<a href="https://agentmods.dev/skills/ilexlyu/ad-proposal-skills/ad-delivery-review"><img src="https://agentmods.dev/badge/skills/ilexlyu/ad-proposal-skills/ad-delivery-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ilexlyu/ad-proposal-skills/ad-delivery-review"><img src="https://agentmods.dev/badge/skills/ilexlyu/ad-proposal-skills/ad-delivery-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.00589 |
| Opus 5 | $0.00041 | $0.00295 |
| Sonnet 5 | $0.00016 | $0.00118 |
| Haiku 4.5 | $0.00008 | $0.00059 |
Grade A, and why
ad-delivery-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
交付质检与复盘
目标：交付前发现风险，交付后沉淀资产。真正高级的提案人不是只做完一份 PPT，而是把这次项目变成下一次可调用的方法。
Inputs
- PPT 内容、提案大纲、客户反馈、最终中标/未中标结果、过程材料。
- 若只是初稿，做交付质检；若项目已结束，做复盘沉淀。
Delivery QA Workflow
- 逻辑检查
- 主线是否清楚；章节是否有因果；每页是否只有一个主判断。
- 证据检查
- 数据是否有来源；年份是否过期；结论是否超出证据。
- 客户检查
- 是否回答客户最关心的问题；语气是否适合决策人。
- 页面检查
- 是否重复、堆字、没有转场、标题不像结论。
- 风险检查
- 哪些判断容易被客户挑战；哪些页面需要备选说法。
Retrospective Workflow
- 提炼本次有效判断：哪些洞察、表达、页面被接受。
- 记录客户偏好：喜欢/不喜欢的逻辑、视觉、语言、案例类型。
- 记录踩坑：资料不足、决策误判、表达不清、流程延误。
- 识别可沉淀资产：资料源、提示词、页面结构、视觉规则、客户画像。
- 建议下一步可封装成哪个 Skill 或 reference。
Output Format
## 交付质检清单
| 问题 | 严重程度 | 所在页/章节 | 修改建议 |
## 风险提示
- ...
## 复盘沉淀
| 类型 | 本次经验 | 下次可复用方法 |
## 可沉淀为 Skill 的资产
- ...
Quality Bar
- 必须区分“这次项目经验”和“下次可复用方法”。
- 不只写总结感想；要产出可执行修改清单和可复用资产。
- 解释每个问题如何影响客户理解、信任或决策，而不是只说“这里不够好”。
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 57 lines · 81 tokens per session scan A 15ee1542e965
ad-delivery-review is a skill published in the GitHub repository ilexlyu/ad-proposal-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 589 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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